Revert "Update linear_model.rst (scikit-learn#12735)"

Xing · Xing · commit 99c93a31cd21 · 2019-04-28T15:16:38.000-04:00
This reverts commit 9a751f3.
diff --git a/doc/modules/linear_model.rst b/doc/modules/linear_model.rst
@@ -786,12 +786,8 @@ non-smooth `penalty="l1"`. This is therefore the solver of choice for sparse
 multinomial logistic regression. It is also the only solver that supports
 `penalty="elasticnet"`.
 
-The "lbfgs" is an optimization algorithm that approximates the 
-Broyden–Fletcher–Goldfarb–Shanno algorithm [8]_, which belongs to
-quasi-Newton methods. The "lbfgs" solver is recommended for use for
-small data-sets but for larger datasets its performance suffers. [9]_
-
-The following table summarizes the penalties supported by each solver:
+In a nutshell, the following table summarizes the penalties supported by
+each solver:
 
 +------------------------------+-----------------+-------------+-----------------+-----------+------------+
 |                              |                       **Solvers**                                        |
@@ -818,7 +814,7 @@ The following table summarizes the penalties supported by each solver:
 +------------------------------+-----------------+-------------+-----------------+-----------+------------+
 
 The "saga" solver is often the best choice but requires scaling. The
-"lbfgs" solver is used by default for historical reasons.
+"liblinear" solver is used by default for historical reasons.
 
 For large dataset, you may also consider using :class:`SGDClassifier`
 with 'log' loss.
@@ -870,12 +866,6 @@ to warm-starting (see :term:`Glossary <warm_start>`).
 
     .. [7] Aaron Defazio, Francis Bach, Simon Lacoste-Julien: `SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives. <https://arxiv.org/abs/1407.0202>`_
 
-    .. [8] https://en.wikipedia.org/wiki/Broyden%E2%80%93Fletcher%E2%80%93Goldfarb%E2%80%93Shanno_algorithm
-
-    .. [9] `"Performance Evaluation of Lbfgs vs other solvers"
-            <http://www.fuzihao.org/blog/2016/01/16/Comparison-of-Gradient-Descent-Stochastic-Gradient-Descent-and-L-BFGS/>`_
-
-
 Stochastic Gradient Descent - SGD
 =================================
 
